CN104751317A - Expert system used in agricultural product supply chain logistics system - Google Patents
Expert system used in agricultural product supply chain logistics system Download PDFInfo
- Publication number
- CN104751317A CN104751317A CN201510180483.2A CN201510180483A CN104751317A CN 104751317 A CN104751317 A CN 104751317A CN 201510180483 A CN201510180483 A CN 201510180483A CN 104751317 A CN104751317 A CN 104751317A
- Authority
- CN
- China
- Prior art keywords
- module
- logistics
- model
- expert system
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004891 communication Methods 0.000 claims abstract description 33
- 238000003306 harvesting Methods 0.000 claims abstract description 31
- 230000003993 interaction Effects 0.000 claims abstract description 15
- 238000004519 manufacturing process Methods 0.000 claims abstract description 8
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000005540 biological transmission Effects 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000000034 method Methods 0.000 description 10
- 238000009826 distribution Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000013068 supply chain management Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 235000013372 meat Nutrition 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 235000021067 refined food Nutrition 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides an expert system used in an agricultural product supply chain logistics system. The expert system used in the agricultural product supply chain logistics system assists in decision making and comprises a GIS (geographic information system) module, a communication module, a model library module, a reasoning module, a network module and a man-machine interaction module, wherein the communication module receives logistics information; harvesting time, logistics time, logistics production plates and an associated model of a decision maker factor are stored in the model library module; the man-machine interaction module is used for receiving the decision maker factor inputted by a decision maker; the reasoning module receives information according to the communication module; the network module is communicated with the internet; and on the basis of the GIS module, the model library module, the network module and the man-machine interaction module, optimal harvesting time and an optimal logistics path are provided and are transmitted to the agricultural product supply chain logistics system through the communication module.
Description
Technical field
The present invention relates to the expert system in logistics progress, more specifically, relate to a kind of expert system used in logistics system.
Background technology
Agricultural-product supply-chain refers to that agricultural product are from purchase, processing, transport, distribution until the chain that forms of this process client's hand of finally sending to, agricultural-product supply-chain management is exactly the various activity and process planning, coordinate, operate, control and optimize the logistics in whole supply chain system, information flow, cash flow, its target is will by the product required for client in the correct time, deliver to correct place with the quality of quantity, requirement accurately and state, and make total cost minimum.The integrated of agricultural-product supply-chain regards an allomeric function process as the miscellaneous service between each enterprises on agricultural-product supply-chain and Joint Enterprises exactly, forms integrated supply chain management system.By effectively controlling logistics, information flow and decision-making stream and coordinate, the supply chain of the supply chain of enterprises and enterprise external is organically integrated and manages, reach overall dynamic optimal target.
In the concrete practice of agricultural products in China supply chain, polytype SC Related Agriculture management all occurs, such as agricultural product raw material supply chain, grain supply chain, supermarket supply chain management of fresh raw agricultural products, novel meat processed food supply chain etc.What lack in current agricultural products in China supply chain integrating process is integrated mechanism, and practice shows, setting up the most effective approach of agricultural-product supply-chain channel integration mechanism is utilize information network technique, uses modern ladder of management and method treatment scheme.Infosystem is the nervous centralis building modernization agricultural-product supply-chain, is flowed fast, accurately and in real time, enterprise can be made actively to make positive reaction to market by information in supply chain system, and guides enterprise to adjust production and operating activities.Therefore, must take information flow as point of penetration, by supply chain by the production in agricultural production, scientific research, buying, transport, processing, distribution until finally send in consumer's hand and regard chain all linked with one another as, to the Supply Operations of upstream, middle processing, storage, movement operation, and the distribution activity in downstream is paid attention on an equal basis, and the logistics in supply chain, cash flow, information flow and technical flow are interacted, form effectively power, thus improve velocity of liquid assets.
In addition, RFID chip obtains huge applying in logistics field.RFID is the powerful technology of a kind of advanced person, and it is based on unlimited radio-frequency technique, can as the means of identification of goods and materials.RFID, except storing except unique identifying number, can also store a large amount of useful information relevant with goods and materials, and wirelessly safe transmission.RFID label tag will be encapsulated in the surface of goods and materials, and it comprises various information and will be stored in the chip of label central authorities.The information comprised in label can be read by the card reader of cooperating with it and upgrade in controlled range.RFID card reader can have multiple interfaces to be connected with the software systems on backstage with mode, carries out more complicated logical process.RFID technique is different from now common barcode technology, and the quantity of information that can carry of RFID label tag is larger, can the citing of communication longer, use-pattern is flexible.
Summary of the invention
Not smooth in order to overcome the information flow existed in existing agricultural-product supply-chain, Information Network upgrades problem not in time, the invention provides a kind of expert system used in logistics, described expert system comprises: GIS module, communication module, model bank module, reasoning module, mixed-media network modules mixed-media and human-computer interaction module, described communication module receiver stream information, the water content in harvest of the various crops of described model bank module stores, logistics time, the correlation model of the logistics place of production and decision maker's factor, described human-computer interaction module is for receiving above-mentioned decision maker's factor of decision maker's input, the information that described reasoning module receives according to communication module, described mixed-media network modules mixed-media is used for and internet communication, based on GIS module, model bank module, mixed-media network modules mixed-media and human-computer interaction module provide best harvest time and logistics route, and be sent to agricultural-product supply-chain logistics system by communication module, described decision maker's factor comprises the amendment information to model bank module information.
Further, communication module comprises GPRS communication module, 3G/4G communication module or wireless data transmission module.
Further, described GIS map comprises electric map module.
Further, described model bank module comprises path computing model, Time Calculation model, crop harvesting model and study enlargement module.
Further, the information updating path computing model, Time Calculation model and the crop harvesting model that receive according to communication module of described study enlargement module.
Further, described mixed-media network modules mixed-media comprises electronic chart update module and model bank module update module.
Further, the information that described reasoning module exports according to path computing model, Time Calculation model and crop harvesting model determines best harvest time and logistics route.
Further, human-computer interaction module comprises keyboard and display.
Expert system of the present invention scientifically can predict the crop harvesting time obtaining maximum return, and the loss avoiding logistics freely not cause with information of supply chain not in time, improves the logistic efficiency of crops supply chain.
Accompanying drawing explanation
Fig. 1 shows the structured flowchart of the expert system used in logistics.
Embodiment
Fig. 1 shows the expert system used in logistics, described expert system comprises: GIS module, communication module, model bank module, reasoning module, mixed-media network modules mixed-media and human-computer interaction module, described communication module receiver stream information, the water content in harvest of the various crops of described model bank module stores, logistics time, the correlation model of the logistics place of production and decision maker's factor, described human-computer interaction module is for receiving above-mentioned decision maker's factor of decision maker's input, the information that described reasoning module receives according to communication module, described mixed-media network modules mixed-media is used for and internet communication, based on GIS module, model bank module, mixed-media network modules mixed-media and human-computer interaction module provide best harvest time and logistics route, and be sent to agricultural-product supply-chain logistics system by communication module, described decision maker's factor comprises the amendment information to model bank module information.
Described communication module comprises GPRS communication module, 3G/4G communication module or wireless data transmission module.
Described GIS map comprises electric map module.
Described model bank module comprises path computing model, Time Calculation model, crop harvesting model, history road conditions model and study enlargement module.Information updating path computing model, Time Calculation model and crop harvesting model that described study enlargement module receives according to communication module.
Wherein, the logistics time within each period of Time Calculation model storage had in the past (or in history) each crops, GIS module (comprises the shortest logistics time according to this various logistics time, each different logistics time such as secondary short logistics time) each crops of marking on electronic chart in each period are various the shortest, secondary short (the shortest logistics time region, region of waiting logistics time, secondary short logistics time region etc. region), path computing model is starting point according to the crops place of production, using above-mentioned zone as territory and for the purpose of each territory, utilize the traffic information that the electric map module of GIS map provides, calculate each time spent by Distance geometry required for feasible path.Road conditions are wherein the traffic informations in this path within the time period that crop harvesting model provides provided according to history road conditions model.
Variety of crops, growth cycle, Meteorological Elements in China judgement harvest time the earliest that crop harvesting model receives according to communication module, each crops place of production is sent.
Path computing model is when path computing, the weight K in each path can be set according to road conditions, and in conjunction with the intelligent path computing of predicted flow rate, motorist's behavioural analysis etc., different according to the congestion level of road conditions by path computing model, give different weights when path computing, calculate corresponding Rational Path.
The information that described reasoning module exports according to path computing model, Time Calculation model and crop harvesting model determines best harvest time and logistics route.Specifically, for certain agricultural product X, each path going to this region that the time that reasoning module acquisition the shortest above-mentioned logistics time occurs, the region of appearance and path computing model obtain and corresponding time.First reasoning module arranges each corresponding time according to the order that path is ascending, and the time utilizing the shortest above-mentioned logistics time to occur deducts the shortest described corresponding time.If the mistiming obtained is within predetermined time, then calculate this cost needed for logistics of path, by comparing of cost and the shortest logistics time, obtain the alternative harvesting harvest time under this situation and alternatives flow path.If exceed predetermined time, then the time that the following short logistics time occurs completes above-mentioned reasoning again.The above-mentioned schedule time should after this crop X allows the time of plucking results.Through above-mentioned reasoning process, finally compare each each alternative harvesting harvest time and alternatives flow path, determine that the best of various crop plucks harvest time and best logistics route will be exported by as best logistics scheme.
Described mixed-media network modules mixed-media comprises electronic chart update module and model bank module update module.The information be updated comprises current logistics traffic information, cartographic information, crops growth cycle etc. information or revises the history of these information.
Human-computer interaction module comprises keyboard and display.Wherein, display is used for the best of output harvesting harvest time and best logistics route to inform decision maker.In addition, decision maker can adjust or revise above-mentioned traffic information, crops growth cycle etc. information by keyboard.
The various embodiments described above are only the preferred embodiment of the present invention, in the art, every based on the changes and improvements in technical solution of the present invention, should not get rid of outside protection scope of the present invention.
Claims (8)
1. the expert system used in logistics, it is characterized in that, described expert system comprises: GIS module, communication module, model bank module, reasoning module, mixed-media network modules mixed-media and human-computer interaction module, described communication module receiver stream information, the water content in harvest of the various crops of described model bank module stores, logistics time, the correlation model of the logistics place of production and decision maker's factor, described human-computer interaction module is for receiving above-mentioned decision maker's factor of decision maker's input, the information that described reasoning module receives according to communication module, described mixed-media network modules mixed-media is used for and internet communication, based on GIS module, model bank module, mixed-media network modules mixed-media and human-computer interaction module provide best harvest time and logistics route, and be sent to agricultural-product supply-chain logistics system by communication module, described decision maker's factor comprises the amendment information to model bank module information.
2. the expert system used in logistics according to claim 1, is characterized in that, communication module comprises GPRS communication module, 3G/4G communication module or wireless data transmission module.
3. the expert system used in logistics according to claim 1, is characterized in that, described GIS map comprises electric map module.
4. the expert system used in logistics according to claim 1, is characterized in that, described model bank module comprises path computing model, Time Calculation model, crop harvesting model and study enlargement module.
5. the expert system used in logistics according to claim 4, is characterized in that, information updating path computing model, Time Calculation model and crop harvesting model that described study enlargement module receives according to communication module.
6. the expert system used in logistics according to claim 4, is characterized in that, described mixed-media network modules mixed-media comprises electronic chart update module and model bank module update module.
7. the expert system used in logistics according to claim 4, is characterized in that, the information that described reasoning module exports according to path computing model, Time Calculation model and crop harvesting model determines best harvest time and logistics route.
8. the expert system used in logistics according to claim 4, is characterized in that, human-computer interaction module comprises keyboard and display.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510180483.2A CN104751317A (en) | 2015-04-16 | 2015-04-16 | Expert system used in agricultural product supply chain logistics system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510180483.2A CN104751317A (en) | 2015-04-16 | 2015-04-16 | Expert system used in agricultural product supply chain logistics system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104751317A true CN104751317A (en) | 2015-07-01 |
Family
ID=53590957
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510180483.2A Pending CN104751317A (en) | 2015-04-16 | 2015-04-16 | Expert system used in agricultural product supply chain logistics system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104751317A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108426884A (en) * | 2018-03-28 | 2018-08-21 | 四川云淞源科技有限公司 | Smart city unmanned plane agricultural product logistics control system |
| CN109711766A (en) * | 2018-11-29 | 2019-05-03 | 深圳春沐源控股有限公司 | Allocator, electronic equipment and storage medium |
| CN118313768A (en) * | 2024-03-11 | 2024-07-09 | 新农创云链(北京)科技有限公司 | Electronic account management system for agricultural product supply chain |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101515345A (en) * | 2009-02-27 | 2009-08-26 | 上海海事大学 | Physical distribution geographical information system |
| CN103383756A (en) * | 2013-07-22 | 2013-11-06 | 浙江省烟草公司绍兴市公司 | Planning method for tobacco logistics distribution routes |
| CN104463540A (en) * | 2014-12-12 | 2015-03-25 | 江阴中科今朝科技有限公司 | Community type logistics transportation management system |
-
2015
- 2015-04-16 CN CN201510180483.2A patent/CN104751317A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101515345A (en) * | 2009-02-27 | 2009-08-26 | 上海海事大学 | Physical distribution geographical information system |
| CN103383756A (en) * | 2013-07-22 | 2013-11-06 | 浙江省烟草公司绍兴市公司 | Planning method for tobacco logistics distribution routes |
| CN104463540A (en) * | 2014-12-12 | 2015-03-25 | 江阴中科今朝科技有限公司 | Community type logistics transportation management system |
Non-Patent Citations (3)
| Title |
|---|
| 刘德军 等: "基于数据挖掘的农业物流地理信息系统的构建", 《物流技术》 * |
| 刘文花: "基于GIS的农产品物流配送系统的设计与实现", 《中国优秀硕士学位论文全文数据库》 * |
| 蒙继华 等: "基于卫星遥感预测作物成熟期的可行性分析", 《遥感技术与应用》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108426884A (en) * | 2018-03-28 | 2018-08-21 | 四川云淞源科技有限公司 | Smart city unmanned plane agricultural product logistics control system |
| CN109711766A (en) * | 2018-11-29 | 2019-05-03 | 深圳春沐源控股有限公司 | Allocator, electronic equipment and storage medium |
| CN118313768A (en) * | 2024-03-11 | 2024-07-09 | 新农创云链(北京)科技有限公司 | Electronic account management system for agricultural product supply chain |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105260791A (en) | Planting plan optimization system and method based on agricultural Internet of Things and big data analysis | |
| CN104766153A (en) | Agricultural things-internet platform architecture | |
| CN104732435A (en) | Agricultural product supply and demand matching system and method | |
| CN104732328A (en) | Agricultural Internet of Things platform system | |
| CN115530054A (en) | Irrigation control method and device, electronic equipment and storage medium | |
| CN104318423A (en) | Dynamic planning method and execution system for finished product warehouse entry progress | |
| González-Estudillo et al. | Optimal planning for sustainable production of Avocado in Mexico | |
| CN104751367A (en) | System for sensing, controlling and tracking breeding quality of breeding pigs in pig farm in whole process | |
| CN104751317A (en) | Expert system used in agricultural product supply chain logistics system | |
| CN102650880A (en) | Intelligent flexible manufacturing system | |
| Cui | Intelligent coordination distribution of the whole supply chain based on the internet of things | |
| Dubey et al. | Study of industry 4.0 pillars and their uses in increasing productivity and reducing logistics defects | |
| Moysiadis et al. | Use of IoT technologies for irrigation and plant protection: The case for Cypriot fruits and vegetables | |
| Gao et al. | Intelligent circulation system modeling using bilateral matching theory under Internet of Things technology | |
| CN118076967A (en) | Demand model based on optimization tree integration | |
| KR101888931B1 (en) | Apparatus and method for consulting distribution information | |
| CN104732372A (en) | Decision support auxiliary system for agricultural product supply chain | |
| Mustashkina et al. | Agricultural development using digital technologies | |
| Deng et al. | Distribution route optimisation of emergency supplies based on improved grey wolf optimisation algorithm | |
| Khan et al. | Optimizing Retail Operations, Inventory Management and Sales Forecasting with Big Data and AI in China | |
| Agarwal | The Impact of Technology on Supply Chain Management and Logistics: An Analytical Study | |
| CN106204310A (en) | Modern agriculture hi-tech incubated development system based on Internet of Things | |
| Matsveichuk et al. | Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian | |
| Saini et al. | Smart agriculture using internet of things: an empirical study | |
| Sattar et al. | Deployment of e-services based contextual smart agro system using internet of things |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150701 |